Approximate Inference Control

author: Marc Toussaint, Machine Learning and Intelligent Data Analysis Group, TU Berlin
published: Jan. 19, 2010,   recorded: December 2009,   views: 371
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Approximate Inference Control (AICO) is a method for solving Stochastic Optimal Control (SOC) problems. The general idea is to think of control as the problem of computing a posterior over trajectories and control signals conditioned on constraints and goals. Since exact inference is infeasible in realistic scenarios, the key for high-speed planning and control algorithms is the choice of approximations. In this talk I will introduce to the general approach, discuss its intimate relations to DDP and the current research on Kalman's duality, and discuss the approximations that we use to get towards real-time planning in high-dimensional robotic systems. I will also mention recent work on using Expectation Propagation and truncated Gaussians for inference under hard constraints and limits as they typically arise in robotics (collision and joint limit constraints).

See Also:

Download slides icon Download slides: nipsworkshops09_toussaint_aic_01.pdf (857.7┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Aidin, March 28, 2012 at 1:47 a.m.:

Wow. I really like this topic. I think the main advantage of this is the integration ability of it. I mean, estimation, motion control and high level reasoning can all be formulated in the same framework.

I think the potential for using sample based representations to get more global solutions would be interesting subject for research...

Thanks for the great lecture
Aidin
RMAL
Ryerson University

Write your own review or comment:

make sure you have javascript enabled or clear this field: